Utveckling av olyckstolerant kärnbränsle

Forskningsprojekt, 2018 – 2023

In the aftermaths of the Fukushima accident, significant efforts have been made world-wide to develop nuclear fuel that would withstand high temperature steam better than the traditional UO2-Zrfuel. The present research on ATFs for light water reactors follows different concepts. The main option today internationally is to provide the conventional Zr alloy cladding with a chromium coating. However, several other coating options are available and one of these is currently being developed in Sweden in a cooperation between Höganäs, Onsala Engineering and Westinghouse Electric Sweden (WSE). In addition, replacement of the UO2fuel pellets by UN could give further advantages. Firstly, it has a higher fraction of uranium, which improves the neutron economy, which will otherwise be slightly degraded by the addition of a coating on the cladding tubes. Secondly, the high temperature oxidation properties can be improved, and the release of harmful substances in case of an accident can be decreased. However, both in the case of coated cladding tubes and UN, improved mechanistic understanding is needed to have a solid basis for implementation.
SAFETY is organized in four work packages (WPs). In WP1, coated claddings are investigated, and in WP2, UN is investigated. The interaction between cladding and UN is studied in WP3, and the effect of irradiation is studied in WP4. In addition, Thuvander (PI) will act as project leader of the whole program, coordinating exchange and interaction between the WPs. The senior scientists of the project are; Chalmers: Mattias Thuvander (Physics), Teodora Retegan, Christian Ekberg and Itai Panas (Chemistry), KTH: Janne Wallenius and Pär Olsson (Physics) and UU: Peter Andersson (Physics and Astronomy).It is intended that theoretical and experimental research in this project be developed self-consistently. The self-consistency evolves from the fact that experiments can show what theory should investigate, while theoretical predictions can be used to better analyse the experimental data, and together, a mechanistic understanding emerges.